summary() generates a summary of the weightit or
weightitMSM object to evaluate the properties of the estimated weights.
plot() plots the distribution of the weights. nobs() extracts the number
of observations.
# S3 method for weightit
summary(object, top = 5L, ignore.s.weights = FALSE, ...)# S3 method for summary.weightit
plot(x, binwidth = NULL, bins = NULL, ...)
# S3 method for weightitMSM
summary(object, top = 5L, ignore.s.weights = FALSE, ...)
# S3 method for summary.weightitMSM
plot(x, binwidth = NULL, bins = NULL, time = 1, ...)
For point treatments (i.e., weightit objects), summary() returns
a summary.weightit object with the following elements:
The range (minimum and maximum) weight for each treatment group.
The units with the greatest weights in each
treatment group; how many are included is determined by top.
The coefficient of variation (standard deviation divided by mean) of the weights in each treatment group and overall.
The mean absolute deviation of the weights in each treatment group and overall divided by the mean of the weights in the corresponding group.
The negative entropy (\(\sum w log(w)\)) of the weights in each treatment group and overall divided by the mean of the weights in the corresponding group.
The number of weights equal to zero.
The effective sample size for each treatment
group before and after weighting. See ESS().
For longitudinal treatments (i.e., weightitMSM objects), summary()
returns a list of the above elements for each treatment period.
plot() returns a ggplot object with a histogram displaying the
distribution of the estimated weights. If the estimand is the ATT or ATC,
only the weights for the non-focal group(s) will be displayed (since the
weights for the focal group are all 1). A dotted line is displayed at the
mean of the weights.
nobs() returns a single number. Note that even units with weights or
s.weights of 0 are included.
a weightit or weightitMSM object; the output of a call to
weightit() or weightitMSM().
how many of the largest and smallest weights to display. Default is 5.
whether or not to ignore sampling weights when
computing the weight summary. If FALSE, the default, the estimated
weights will be multiplied by the sampling weights (if any) before values
are computed.
For plot(), additional arguments passed to graphics::hist() to
determine the number of bins, though ggplot2::geom_histogram() is
actually used to create the plot.
a summary.weightit or summary.weightitMSM object; the output of
a call to summary.weightit() or summary.weightitMSM().
arguments passed to ggplot2::geom_histogram() to
control the size and/or number of bins.
numeric; the time point for which to display the distribution
of weights. Default is to plot the distribution for the first time points.
weightit(), weightitMSM(), summary()
# See example at ?weightit or ?weightitMSM
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